Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
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MP3•Episoder hjem
Manage episode 295500885 series 2921809
Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
What is vmap? How is it implemented? How does our implementation compare to JAX's? What is a good way of understanding what vmap does? What's up with random numbers? Why are there some issues with the vmap that PyTorch currently ships?
Further reading.
- Tracking issue for vmap support https://github.com/pytorch/pytorch/issues/42368
- BatchedTensor source code https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/BatchedTensorImpl.h , logical-physical transformation helper code https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/VmapTransforms.h (well documented, worth a read)
- functorch, the better, more JAX-y implementation of vmap https://github.com/facebookresearch/functorch
- Autodidax https://jax.readthedocs.io/en/latest/autodidax.html which contains a super simple vmap implementation that is a good model for the internal implementation that PyTorch has
83 episoder
MP3•Episoder hjem
Manage episode 295500885 series 2921809
Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
What is vmap? How is it implemented? How does our implementation compare to JAX's? What is a good way of understanding what vmap does? What's up with random numbers? Why are there some issues with the vmap that PyTorch currently ships?
Further reading.
- Tracking issue for vmap support https://github.com/pytorch/pytorch/issues/42368
- BatchedTensor source code https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/BatchedTensorImpl.h , logical-physical transformation helper code https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/VmapTransforms.h (well documented, worth a read)
- functorch, the better, more JAX-y implementation of vmap https://github.com/facebookresearch/functorch
- Autodidax https://jax.readthedocs.io/en/latest/autodidax.html which contains a super simple vmap implementation that is a good model for the internal implementation that PyTorch has
83 episoder
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